Synthetic Brew

Open-source agent infrastructure for building, running, and orchestrating AI agents across real software development workflows.

[Synthetic Brew @SyntheticAIInc]

We launched Synthetic Brew on Product Hunt today 🚀

Synthetic Brew is an open-source runtime for building production-grade AI agents that are grounded in your data, run on your infrastructure, and avoid vendor lock-in.

Some love would mean a lot. Please upvote, share, and tell us what you think:

https://t.co/TqUswiCSA1

#AI #AIAgents #OpenSource #ProductHunt

[Synthetic Brew @SyntheticAIInc]

Big day for us: Kilo IoT just launched AIoT — and it's powered by Synthetic Brew. 🚀

This is what we've been building toward: Synthetic Brew running in a real product, in production, doing real work.

Here's what that looks like. The Kilo IoT platform is powerful but deeply technical — the kind of system that used to need a trained integrator to set up. Now, with Synthetic Brew underneath, you just talk to it. Describe what you want in plain English and it configures the devices, writes the automation rules, and sets up the alerts for you. It makes IoT smart — an experienced integrator by your side, always on, that doesn't just answer questions, it does the work.

And that's the whole point of Synthetic Brew: exactly the way it's built into Kilo, you can build it into any business.

The hard part of AI was never the model — it's the infrastructure underneath that makes it reliable: retrieval, knowledge graphs, tools, memory, permissions, multi-agent orchestration, and a record of every action. Get that wrong and you ship a confident liar. Get it right and you ship an agent that actually knows your world and can act in it.

Synthetic Brew puts all of that in the box — open-source and self-hosted:
→ Describe the agent you need in plain English; it builds, deploys, and orchestrates it
→ Grounded with RAG + knowledge graphs, so it doesn't make things up
→ Your tools, your data, any LLM, one Docker command — no lock-in, no per-token markup

Kilo is the proof. Your product could be next.

⭐ Star it: https://t.co/h045I6aGcT
Dig in: https://t.co/XQExJ9tb6v
No in-house AI team? We'll build it into your product for you: https://t.co/HdNAnSlEBO

#SyntheticBrew #OpenSource #AIagents #AI #AIAgentRuntime

[Synthetic Brew @SyntheticAIInc]

A 7-question pre-flight before you greenlight an AI feature:

grounding · memory · tools · permissions · approvals · failure · observability

Answer all 7 → you have a plan.
Can't → you have a demo.

Full checklist: https://t.co/pYGDn7oWrG

#SyntheticBrew #AI #AIEngineering

[Synthetic Brew @SyntheticAIInc]

Your AI demo works. That's the problem.

The model was never the hard part. The gap between "impressive demo" and "thing I'd trust with a customer" is the 90% nobody shows on stage.

A thread on what it actually takes to put an AI agent into production 🧵

[Synthetic Brew @SyntheticAIInc]

Approval gates sound trivial until you build them.

The agent has to suspend mid-task, hand a decision to a person, and resume cleanly with that answer folded in — maybe the next day.

Easy to describe. Annoying to get right.

[Synthetic Brew @SyntheticAIInc]

AI features are no longer “nice to have.”

Customers now expect software to understand context, automate work, answer questions, trigger actions, and adapt to their business logic.

But there is a problem most teams are quietly running into:

Shipping reliable AI features is not just a product problem anymore.

It is an infrastructure problem.

Agents need tools.
They need memory.
They need knowledge retrieval.
They need permissions.
They need approval gates.
They need model routing.
They need logs, traces, observability, and cost control.
They need to run inside real products, not just demos.
And most companies do not have six months to build all of that before shipping their first useful AI workflow.

That is why we built Synthetic Brew.

Synthetic Brew is an open-source AI agent runtime for teams that want to build agents, copilots, assistants, and automation workflows without becoming AI infrastructure companies first.

Describe what you want in plain English:
“Build a support agent that knows our help center, checks customer history, resolves simple tickets, and escalates refunds over $200 for human approval.”

Synthetic Brew’s AI Builder wires up the agents, tools, memory, flows, and approval gates needed to make it work.

The Builder itself runs on the same runtime your agents use, so every workflow it creates is built on real production infrastructure, not a separate demo layer.

Synthetic Brew is self-hosted.

Your data stays on your infrastructure.
Your provider keys stay under your control.
Your AI costs stay your decision.
Your product architecture stays yours.

Use OpenAI, Anthropic, Google AI, Groq, DeepSeek, Ollama, or any OpenAI-compatible endpoint.

Start with one Docker command.
Open the dashboard.
Add your LLM key.
Describe the workflow you want.
Test your agent.

This is for product teams, SaaS companies, internal platform teams, and builders who know AI belongs in their product but do not want to rebuild the same agent infrastructure from scratch every time.

AI is becoming a default expectation in software.

The question is not whether your product will need AI.

It will.

The real question is:
Who controls the infrastructure underneath it?
With Synthetic Brew, the answer can be:
Your team.
Your infrastructure.
Your data.
Your agents.

Synthetic Brew is now open source.

Try it. Break it. Build with it. Tell us what is missing.

Website: https://t.co/CHif4bXhiO
GitHub: https://t.co/JHXD4W7tLj

#AI #AIAgents #OpenSource #AgentInfrastructure #LLMOps #AIInfrastructure